library(drc)
library(readxl)
library(SuperExactTest)
library(VennDiagram)
library(DT)
library(Glimma)
load("~/Documents/RNAseq/RNAseq_S2/RNaseq_environnement.RData")tab_RNAseq_S2_embryo = lrt.2.tables$ActVsContr[rownames(lrt.2.tables$ActVsContr)%in%data_embryon_epiderm$SYMBOL,]
tab_RNAseq_S2_embryo$neglog10Pvalue = -log10(tab_RNAseq_S2_embryo$PValue)
#datatable(tab_RNAseq_S2_embryo,rownames = T,filter = "top", caption = "Expression des cibles embryonnaires dans les cellules S2 ")
count_RNA = RNAseq_norm$counts[rownames(RNAseq_norm$counts)%in%rownames(tab_RNAseq_S2_embryo),]
diffGeneActContr_embryo = diffGeneActContr[rownames(diffGeneActContr)%in%rownames(tab_RNAseq_S2_embryo),]
glimmaXY(tab_RNAseq_S2_embryo$logFC, tab_RNAseq_S2_embryo$neglog10Pvalue,counts = count_RNA , status = diffGeneActContr_embryo, groups = groups,status.cols = c("red","grey","green"), main = "Gènes cibles embryonnaire dans les S2 ( ActvsCtrl)", ylab = "neglog10Pvalue", xlab = "logFC")tab_RNAseq_S2_embryo_rep = lrt.2.tables$RepVsContr[rownames(lrt.2.tables$RepVsContr)%in%data_embryon_epiderm$SYMBOL,]
tab_RNAseq_S2_embryo_rep$neglog10Pvalue = -log10(tab_RNAseq_S2_embryo_rep$PValue)
#datatable(tab_RNAseq_S2_embryo_rep,rownames = T,filter = "top", caption = "Expression des cibles embryonnaires dans les cellules S2 ")
count_RNA = RNAseq_norm$counts[rownames(RNAseq_norm$counts)%in%rownames(tab_RNAseq_S2_embryo_rep),]
diffGeneRepContr_embryo = diffGeneRepContr[rownames(diffGeneRepContr)%in%rownames(tab_RNAseq_S2_embryo_rep),]
glimmaXY(tab_RNAseq_S2_embryo_rep$logFC, tab_RNAseq_S2_embryo_rep$neglog10Pvalue,counts = count_RNA , status = diffGeneRepContr_embryo, groups = groups,status.cols = c("red","grey","green"), main = "Gènes cibles embryonnaire dans les S2 (RepvsCtrl)", ylab = "neglog10Pvalue", xlab = "logFC")tab_RNAseq_S2_embryo_actrep = lrt.2.tables$ActVsRep[rownames(lrt.2.tables$ActVsRep)%in%data_embryon_epiderm$SYMBOL,]
tab_RNAseq_S2_embryo_actrep$neglog10Pvalue = -log10(tab_RNAseq_S2_embryo_actrep$PValue)
#datatable(tab_RNAseq_S2_embryo_actrep,rownames = T,filter = "top", caption = "Expression des cibles embryonnaires dans les cellules S2 ")
count_RNA = RNAseq_norm$counts[rownames(RNAseq_norm$counts)%in%rownames(tab_RNAseq_S2_embryo_actrep),]
diffGeneActRep_embryo = diffGeneActvsRep[rownames(diffGeneActvsRep)%in%rownames(tab_RNAseq_S2_embryo_actrep),]
glimmaXY(tab_RNAseq_S2_embryo_actrep$logFC, tab_RNAseq_S2_embryo_actrep$neglog10Pvalue,counts = count_RNA , status = diffGeneActRep_embryo, groups = groups,status.cols = c("red","grey","green"), main = "Gènes cibles embryonnaire dans les S2 (ActvsRep)", ylab = "neglog10Pvalue", xlab = "logFC")tab_anno_embryonnaire_ActCtrl <- read.delim("~/Documents/RNAseq/RNAseq_S2/Comparaison_S2Embryon/tab_anno_embryonnaire_ActCtrl.txt", quote="")
tab_anno_embryonnaire_ActCtrl$neglog10Pvalue = -log10(tab_anno_embryonnaire_ActCtrl$Pvalue)
glimmaXY(tab_anno_embryonnaire_ActCtrl$logFC, tab_anno_embryonnaire_ActCtrl$neglog10Pvalue,counts = RNAseq_norm$counts , status = tab_anno_embryonnaire_ActCtrl$genesS2, groups = groups,status.cols = c("grey","grey","purple"), main = "Gènes cibles embryonnaire dans les S2 (ActvsCtrl)", ylab = "neglog10Pvalue", xlab = "logFC")tab_anno_embryonnaire_RepCtrl <- read.delim("~/Documents/RNAseq/RNAseq_S2/Comparaison_S2Embryon/tab_anno_embryonnaire_RepCtrl.txt", quote="")
tab_anno_embryonnaire_RepCtrl$neglog10Pvalue = -log10(tab_anno_embryonnaire_RepCtrl$Pvalue )
glimmaXY(tab_anno_embryonnaire_RepCtrl$logFC, tab_anno_embryonnaire_RepCtrl$neglog10Pvalue,counts = RNAseq_norm$counts , status = tab_anno_embryonnaire_RepCtrl$genesS2, groups = groups,status.cols = c("grey","grey","purple"), main = "Gènes cibles embryonnaire dans les S2 (RepvsCtrl)", ylab = "neglog10Pvalue", xlab = "logFC")tab_anno_embryonnaire_ActRep <- read.delim("~/Documents/RNAseq/RNAseq_S2/Comparaison_S2Embryon/tab_anno_embryonnaire_ActRep.txt", quote="")
tab_anno_embryonnaire_ActRep$neglog10Pvalue = -log10(tab_anno_embryonnaire_ActRep$Pvalue )
glimmaXY(tab_anno_embryonnaire_ActRep$logFC, tab_anno_embryonnaire_ActRep$neglog10Pvalue,counts = RNAseq_norm$counts , status = tab_anno_embryonnaire_ActRep$genesS2, groups = groups,status.cols = c("grey","grey","purple"), main = "Gènes cibles embryonnaire dans les S2 (ActvsRep)", ylab = "neglog10Pvalue", xlab = "logFC")tab_genesS2_dansembryon = read.table("expressiongenesciblesS2dansembryon.csv",header = T )
rownames(tab_genesS2_dansembryon) = tab_genesS2_dansembryon$ProbeSetID
tab_genesS2_dansembryon$NegLog10Pval = -log10(tab_genesS2_dansembryon$p.value)
glimmaXY(tab_genesS2_dansembryon$logFC, -log10(tab_genesS2_dansembryon$p.value),status = tab_genesS2_dansembryon$statut,xlab = "logFC", ylab = "NegLog10Pval", anno = tab_genesS2_dansembryon, display.columns = c("gene","GeneName","logFC","NegLog10Pval"))sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin19.6.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /usr/local/Cellar/openblas/0.3.13/lib/libopenblasp-r0.3.13.dylib
## LAPACK: /usr/local/Cellar/r/4.0.3_2/lib/R/lib/libRlapack.dylib
##
## Random number generation:
## RNG: Mersenne-Twister
## Normal: Inversion
## Sample: Rounding
##
## locale:
## [1] fr_FR.UTF-8/fr_FR.UTF-8/fr_FR.UTF-8/C/fr_FR.UTF-8/fr_FR.UTF-8
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] Glimma_2.0.0 DT_0.17 VennDiagram_1.6.20
## [4] futile.logger_1.4.3 SuperExactTest_1.0.7 readxl_1.3.1
## [7] drc_3.0-1 MASS_7.3-53.1
##
## loaded via a namespace (and not attached):
## [1] TH.data_1.0-10 colorspace_2.0-0
## [3] ellipsis_0.3.1 rio_0.5.16
## [5] XVector_0.30.0 GenomicRanges_1.42.0
## [7] ChIPpeakAnno_3.24.1 bit64_4.0.5
## [9] AnnotationDbi_1.52.0 fansi_0.4.2
## [11] mvtnorm_1.1-1 xml2_1.3.2
## [13] codetools_0.2-18 splines_4.0.3
## [15] cachem_1.0.4 geneplotter_1.68.0
## [17] knitr_1.31 jsonlite_1.7.2
## [19] Rsamtools_2.6.0 annotate_1.68.0
## [21] dbplyr_2.1.0 png_0.1-7
## [23] graph_1.68.0 compiler_4.0.3
## [25] httr_1.4.2 lazyeval_0.2.2
## [27] assertthat_0.2.1 Matrix_1.3-2
## [29] fastmap_1.1.0 limma_3.46.0
## [31] formatR_1.7 prettyunits_1.1.1
## [33] htmltools_0.5.1.1 tools_4.0.3
## [35] gtable_0.3.0 glue_1.4.2
## [37] GenomeInfoDbData_1.2.4 dplyr_1.0.4
## [39] rappdirs_0.3.3 Rcpp_1.0.6
## [41] carData_3.0-4 Biobase_2.50.0
## [43] cellranger_1.1.0 jquerylib_0.1.3
## [45] Biostrings_2.58.0 vctrs_0.3.6
## [47] multtest_2.46.0 rtracklayer_1.50.0
## [49] xfun_0.21 stringr_1.4.0
## [51] openxlsx_4.2.3 lifecycle_1.0.0
## [53] ensembldb_2.14.0 gtools_3.8.2
## [55] XML_3.99-0.5 edgeR_3.32.1
## [57] zlibbioc_1.36.0 zoo_1.8-8
## [59] scales_1.1.1 BSgenome_1.58.0
## [61] ProtGenerics_1.22.0 hms_1.0.0
## [63] MatrixGenerics_1.2.1 RBGL_1.66.0
## [65] parallel_4.0.3 SummarizedExperiment_1.20.0
## [67] sandwich_3.0-0 AnnotationFilter_1.14.0
## [69] lambda.r_1.2.4 RColorBrewer_1.1-2
## [71] yaml_2.2.1 curl_4.3
## [73] memoise_2.0.0 ggplot2_3.3.3
## [75] sass_0.3.1 biomaRt_2.46.3
## [77] stringi_1.5.3 RSQLite_2.2.3
## [79] genefilter_1.72.1 S4Vectors_0.28.1
## [81] plotrix_3.8-1 GenomicFeatures_1.42.1
## [83] BiocGenerics_0.36.0 zip_2.1.1
## [85] BiocParallel_1.24.1 GenomeInfoDb_1.26.2
## [87] rlang_0.4.10 pkgconfig_2.0.3
## [89] matrixStats_0.58.0 bitops_1.0-6
## [91] evaluate_0.14 lattice_0.20-41
## [93] purrr_0.3.4 GenomicAlignments_1.26.0
## [95] htmlwidgets_1.5.3 bit_4.0.4
## [97] tidyselect_1.1.0 magrittr_2.0.1
## [99] DESeq2_1.30.1 R6_2.5.0
## [101] IRanges_2.24.1 generics_0.1.0
## [103] multcomp_1.4-16 DelayedArray_0.16.1
## [105] DBI_1.1.1 pillar_1.5.0
## [107] haven_2.3.1 foreign_0.8-81
## [109] KEGGREST_1.30.1 survival_3.2-7
## [111] abind_1.4-5 RCurl_1.98-1.2
## [113] tibble_3.0.6 crayon_1.4.1
## [115] car_3.0-10 futile.options_1.0.1
## [117] utf8_1.1.4 BiocFileCache_1.14.0
## [119] rmarkdown_2.7 progress_1.2.2
## [121] locfit_1.5-9.4 data.table_1.14.0
## [123] blob_1.2.1 forcats_0.5.1
## [125] digest_0.6.27 xtable_1.8-4
## [127] regioneR_1.22.0 openssl_1.4.3
## [129] stats4_4.0.3 munsell_0.5.0
## [131] bslib_0.2.4 askpass_1.1Menoret, Delphine, Marc Santolini, Isabelle Fernandes, Rebecca Spokony, Jennifer Zanet, Ignacio Gonzalez, Yvan Latapie, et al. 2013. “Genome-wide analyses of Shavenbaby target genes reveals distinct features of enhancer organization.” Genome Biology 14 (8): R86. https://doi.org/10.1186/gb-2013-14-8-r86.